THDI Measurement System of Home Energy Signal Based on IoT

Neto, Iran Macedo B.; Lopes, Amanda I.; Brito, Mateus M. De Assis; Silva, Diego R. C.; Nogueira, Marco Aurélio; Rodrigues, Marconi C.

doi:10.1109/metroi4.2018.8428311

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…An example of an IoT architecture than could be applied in complex smart environments, such as smart campuses, is the SaIoT (Smart Automation using Internet of Things) architecture [37][38][39], which was first developed for smart home and building applications. This architecture has been successfully in many measurement applications, such as water and electric energy consumption [40,41], by also including the computation of proper KPIs.…”

Section: The Role Of Iot In Smart Environmentsmentioning

confidence: 99%

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

et al. 2020

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The application of the most recent advances of the Internet-of-Things (IoT) technology to the automation of buildings is emerging as a promising solution to achieve greater efficiencies in energy consumption, and to allow the realization of sustainable models. The application of IoT has been demonstrated as effective in many fields, such as confirmed, for instance, by the Industry 4.0 concepts, which are revolutionizing modern production chains. By following this approach, the use of distributed control architectures and of IoT technologies (both wired and wireless) would result in effective solutions for the management of smart environments composed of groups of buildings, such as campuses. In this case, heterogeneous IoT solutions are typically adopted to satisfy the requirements of the very diverse possible scenarios (e.g., indoor versus outdoor coverage, mobile versus fixed nodes, just to mention a few), making their large-scale integration cumbersome. To cope with this issue, this paper presents an IoT architecture able to transparently manage different communication protocols in smart environments, and investigates its possible application for the monitoring and control of distributed energy resources in a smart campus. In particular, a use–case focused on the integration of the Long Range Wide Area Network (LoRaWAN) technology is considered to cope with heterogeneous indoor and outdoor communication scenarios. The feasibility analysis of the proposed solution is carried out by computing the scalability limits of the approach, based on the proposed smart campus data model. The results of the study showed that the proposed solution would be able to manage more than 10,000 nodes. An experimental validation of the LoRaWAN technology confirms its suitability in terms of coverage and latency, with a minimum LoRaWAN cell coverage range of 250 m, and a communication latency of about 400 ms. Finally, the advantages of the proposed solution in the supervision and management of a PV system are highlighted in a real-world scenario.

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Section: The Role Of Iot In Smart Environmentsmentioning

confidence: 99%

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

et al. 2020

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“…Research that decreasing harmonic also has been done in research [5][6][7]. However, the impacts of harmonic tend to be ignored by users because there is no monitoring system for the electrical signal quality provided by the electrical providers [8]. Even though electrical energy monitoring system becomes an important subject to give information to users [9].…”

Section: Introductionmentioning

confidence: 99%

Realtime IoT based Harmonics Monitoring System Review with Potential Low-Cost Devices with Experimental Case Study

Purnomo¹,

Aripriharta²,

Handayani³

et al. 2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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“…Voltage and RMS current are calculated to evaluate the energy consumption in each load, but it does not detect voltage drops. In [14] the ESP8266 module is used for THDi (Current Total Harmonic Distortion) via FFT (the Fast Fourier Transform), and the current RMS value for each load is given via the RMS method. The values of these parameters are transmitted to a server and implemented through a Raspberry Pi, using the MQTT (Message Queuing Telemetry Transport) protocol so that the user can monitor the energy quality signal and know the electrical faults of a load or the network.…”

Section: Introductionmentioning

confidence: 99%

A Memory-Efficient True-RMS Estimator in a Limited-Resources Hardware

et al. 2019

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More and more human activities are increasingly dependent on the power quality energy they are supplied. In particular, those loads that have an electronic switching mode power supply (SMPS) and microcontrollers or microprocessors are very susceptible to de-energization of the AC line as voltage monitoring takes place on the DC end and may not have enough time to cope with a voltage event involving energy loss. There have been many proposals that analyze power quality or current consumption, even those using limited resources hardware or the classic formulas of discrete root mean square (RMS). In this proposal, an approximation to the problem by means of an RMS value estimator that uses as a base a microcontroller of basic range and low economic cost and algorithms of calculation of very low computational cost that elude complex arithmetic operation to controllers, such as powers or roots, is presented. The results of the experiments to which the proposal was subjected show its ability to provide an estimation of the RMS value of voltage with sufficient precision or an event alarm in less time than the options analyzed. The validation tests and functional comparison of the prototype which were carried out support its viability.

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THDI Measurement System of Home Energy Signal Based on IoT

Cited by 3 publications

References 11 publications

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

Realtime IoT based Harmonics Monitoring System Review with Potential Low-Cost Devices with Experimental Case Study

A Memory-Efficient True-RMS Estimator in a Limited-Resources Hardware

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